This book provides an introduction to three of the main tools used in the development of bioinformatics software — Perl, R, and MySQL — and explains how these can be used together to tackle the ...
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This book provides an introduction to three of the main tools used in the development of bioinformatics software — Perl, R, and MySQL — and explains how these can be used together to tackle the complex data-driven challenges that typify modern biology. The book is intended to provide the reader with the knowledge and confidence needed to create databases, to write programs to analyse and visualise data, and to develop interactive web-based applications. Platform-independent examples are provided throughout, making the book suitable for users of Windows, Mac OS or Linux.Less

Building Bioinformatics Solutions

Conrad BessantDarren OakleyIan Shadforth

Published in print: 2014-01-16

This book provides an introduction to three of the main tools used in the development of bioinformatics software — Perl, R, and MySQL — and explains how these can be used together to tackle the complex data-driven challenges that typify modern biology. The book is intended to provide the reader with the knowledge and confidence needed to create databases, to write programs to analyse and visualise data, and to develop interactive web-based applications. Platform-independent examples are provided throughout, making the book suitable for users of Windows, Mac OS or Linux.

This book discusses the change in use of statistics in ecology—especially the increased use (over the last two decades) of more sophisticated statistical and computational methods. This book also ...
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This book discusses the change in use of statistics in ecology—especially the increased use (over the last two decades) of more sophisticated statistical and computational methods. This book also highlights the contribution of statistical modeling to knowledge acquisition as an important way of abstracting ecological questions into mathematical models, and its role in the research cycle currently used by most ecologists. The book reviews briefly the logic and key parts of statistical linear models, as they form the conceptual foundation of most of the methods discussed in the book. Finally, it explains the book’s organization, the background required for readers, and strategies for getting the most out of this intermediate-level book.Less

Ecological Statistics : Contemporary theory and application

Published in print: 2015-01-29

This book discusses the change in use of statistics in ecology—especially the increased use (over the last two decades) of more sophisticated statistical and computational methods. This book also highlights the contribution of statistical modeling to knowledge acquisition as an important way of abstracting ecological questions into mathematical models, and its role in the research cycle currently used by most ecologists. The book reviews briefly the logic and key parts of statistical linear models, as they form the conceptual foundation of most of the methods discussed in the book. Finally, it explains the book’s organization, the background required for readers, and strategies for getting the most out of this intermediate-level book.

Learning how to get answers from data is an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting changes in data management and ...
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Learning how to get answers from data is an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting changes in data management and analysis during the last decade has been the growth of open source software. The open source statistics and programming language R has emerged as a critical component of any researcher's toolbox. Indeed, R is rapidly becoming the standard software for analyses, graphical presentations, and programming in the biological sciences. This book provides a functional introduction to R. While teaching how to import, explore, graph, and analyse data, it keeps readers focused on their ultimate goals — communicating their data in oral presentations, posters, papers, and reports. It also provides a consistent method (workflow) for using R that is simple, efficient, reliable, accurate, and reproducible. The material in the book reproduces the engaging and sometimes humorous nature of the three-day course on which it is based.Less

Getting Started with R : An introduction for biologists

Andrew P. BeckermanOwen L. Petchey

Published in print: 2012-05-24

Learning how to get answers from data is an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting changes in data management and analysis during the last decade has been the growth of open source software. The open source statistics and programming language R has emerged as a critical component of any researcher's toolbox. Indeed, R is rapidly becoming the standard software for analyses, graphical presentations, and programming in the biological sciences. This book provides a functional introduction to R. While teaching how to import, explore, graph, and analyse data, it keeps readers focused on their ultimate goals — communicating their data in oral presentations, posters, papers, and reports. It also provides a consistent method (workflow) for using R that is simple, efficient, reliable, accurate, and reproducible. The material in the book reproduces the engaging and sometimes humorous nature of the three-day course on which it is based.

Getting Started with R deals with learning how to get answers from data, an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting ...
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Getting Started with R deals with learning how to get answers from data, an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting developments in data management, quantitative analysis, and visualization was the growth of the open source application R. This statistics and programming language has emerged as a critical component of biologists’, and many other scientists’, toolboxes. R is rapidly becoming standard software for data manipulation, visualization, and analysis. This book provides a functional introduction for biologists new to R. While teaching how to import, visualize, and analyse, it keeps readers focused on their ultimate goals … to communicate their data and analyses in presentations, posters, papers, websites, and reports. It provides a consistent approach and workflow for using R, one that is simple, efficient, intuitive, reliable, accurate, and reproducible. The material in the book reproduces the engaging and sometimes humorous nature of the three-day course on which it is based. What is different in the second edition? It has been entirely rewritten to accommodate several new developments in R and changes made in teaching the course. Chapters have been added on preparing data for R, on analyses of more experimental designs (regression and one-way and two-way ANOVA, in addition to the old ANCOVA example), and on generalized linear models. The book also uses as default a popular, new set of tools for managing data and producing graphs via the add-on packages dplyr and ggplot2. There are now three authors.Less

Getting Started with R : An Introduction for Biologists

Andrew BeckermanDylan ChildsOwen Petchey

Published in print: 2017-01-26

Getting Started with R deals with learning how to get answers from data, an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting developments in data management, quantitative analysis, and visualization was the growth of the open source application R. This statistics and programming language has emerged as a critical component of biologists’, and many other scientists’, toolboxes. R is rapidly becoming standard software for data manipulation, visualization, and analysis. This book provides a functional introduction for biologists new to R. While teaching how to import, visualize, and analyse, it keeps readers focused on their ultimate goals … to communicate their data and analyses in presentations, posters, papers, websites, and reports. It provides a consistent approach and workflow for using R, one that is simple, efficient, intuitive, reliable, accurate, and reproducible. The material in the book reproduces the engaging and sometimes humorous nature of the three-day course on which it is based. What is different in the second edition? It has been entirely rewritten to accommodate several new developments in R and changes made in teaching the course. Chapters have been added on preparing data for R, on analyses of more experimental designs (regression and one-way and two-way ANOVA, in addition to the old ANCOVA example), and on generalized linear models. The book also uses as default a popular, new set of tools for managing data and producing graphs via the add-on packages dplyr and ggplot2. There are now three authors.

Invasion Dynamics depicts how non-native species spread and perform in their novel ranges and how recipient socio-ecological systems are reshaped and how they respond to the new incursions. It ...
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Invasion Dynamics depicts how non-native species spread and perform in their novel ranges and how recipient socio-ecological systems are reshaped and how they respond to the new incursions. It collects evidence for grouping patterns of spread into four types and three associated phenomena, discusses candidate explanations for each pattern, and introduces analytic tools for capturing and forecasting invasion dynamics. Special attention is given to the potential mechanisms of boosted range expansion and nonequilibrium demographic dynamics during invasion. The diverse mechanisms that drive direct and mediated biotic interactions between invaders and resident species are elucidated, and triggers of potential regime shifts in recipient ecosystems are identified. It further explores the ways in which local and regional species assemblages are reshuffled and reorganized. Efficient management of invasions requires not only insights on invasion dynamics across scales but also objective assessment of ecological and economic impacts, as well as sound protocols for prioritizing and optimizing management effort. Biological invasions, therefore, involve more than the actions of invaders and reactions of invaded ecosystems; they represent a co-evolving complex adaptive system with emergent features of network complexity and invasibility. Invasions are thus a formidable force that acts in concert with other facets of global change to initiate the adaptive wheel of panarchy and shape the altered biosphere in the Anthropocene.Less

Invasion Dynamics

Cang HuiDavid M. Richardson

Published in print: 2017-01-19

Invasion Dynamics depicts how non-native species spread and perform in their novel ranges and how recipient socio-ecological systems are reshaped and how they respond to the new incursions. It collects evidence for grouping patterns of spread into four types and three associated phenomena, discusses candidate explanations for each pattern, and introduces analytic tools for capturing and forecasting invasion dynamics. Special attention is given to the potential mechanisms of boosted range expansion and nonequilibrium demographic dynamics during invasion. The diverse mechanisms that drive direct and mediated biotic interactions between invaders and resident species are elucidated, and triggers of potential regime shifts in recipient ecosystems are identified. It further explores the ways in which local and regional species assemblages are reshuffled and reorganized. Efficient management of invasions requires not only insights on invasion dynamics across scales but also objective assessment of ecological and economic impacts, as well as sound protocols for prioritizing and optimizing management effort. Biological invasions, therefore, involve more than the actions of invaders and reactions of invaded ecosystems; they represent a co-evolving complex adaptive system with emergent features of network complexity and invasibility. Invasions are thus a formidable force that acts in concert with other facets of global change to initiate the adaptive wheel of panarchy and shape the altered biosphere in the Anthropocene.

This book summarizes the statistical models and computational algorithms for comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, and ...
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This book summarizes the statistical models and computational algorithms for comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, and statistical phylogeography. The book presents and explains the models of nucleotide, amino acid, and codon substitution, and their use in calculating pairwise sequence distances and in reconstruction of phylogenetic trees. All major methods for phylogeny reconstruction are covered in detail, including neighbour joining, maximum parsimony, maximum likelihood, and Bayesian methods. Using motivating examples, the book includes a comprehensive introduction to Bayesian computation using Markov chain Monte Carlo (MCMC). Advanced topics include estimation of species divergence times using the molecular clock, detection of molecular adaptation, simulation of molecular evolution, as well as species tree estimation and species delimitation using genomic sequence data.Less

Molecular Evolution : A Statistical Approach

Ziheng Yang

Published in print: 2014-05-15

This book summarizes the statistical models and computational algorithms for comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, and statistical phylogeography. The book presents and explains the models of nucleotide, amino acid, and codon substitution, and their use in calculating pairwise sequence distances and in reconstruction of phylogenetic trees. All major methods for phylogeny reconstruction are covered in detail, including neighbour joining, maximum parsimony, maximum likelihood, and Bayesian methods. Using motivating examples, the book includes a comprehensive introduction to Bayesian computation using Markov chain Monte Carlo (MCMC). Advanced topics include estimation of species divergence times using the molecular clock, detection of molecular adaptation, simulation of molecular evolution, as well as species tree estimation and species delimitation using genomic sequence data.

Statistics is a fundamental component of the scientific toolbox, but learning the basics of this area of mathematics is one of the most challenging parts of a research training. This book gives an ...
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Statistics is a fundamental component of the scientific toolbox, but learning the basics of this area of mathematics is one of the most challenging parts of a research training. This book gives an up-to-date introduction to the classical techniques and modern extensions of linear model analysis—one of the most useful approaches in the analysis of scientific data in the life and environmental sciences. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach using information criteria. The book is based on the use of the open-source R programming language for statistics and graphics that is rapidly becoming the lingua franca in many areas of science. Statistics is introduced through worked analyses performed in R using interesting data sets from ecology, evolutionary biology, and environmental science. The data sets and R scripts are available as supporting material.Less

The New Statistics with R : An Introduction for Biologists

Andy Hector

Published in print: 2015-01-29

Statistics is a fundamental component of the scientific toolbox, but learning the basics of this area of mathematics is one of the most challenging parts of a research training. This book gives an up-to-date introduction to the classical techniques and modern extensions of linear model analysis—one of the most useful approaches in the analysis of scientific data in the life and environmental sciences. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach using information criteria. The book is based on the use of the open-source R programming language for statistics and graphics that is rapidly becoming the lingua franca in many areas of science. Statistics is introduced through worked analyses performed in R using interesting data sets from ecology, evolutionary biology, and environmental science. The data sets and R scripts are available as supporting material.

The book presents a hands-on introductory guide to DNA sequence analysis. This can be depicted as a linear map of As, Cs, Gs, and Ts; however, such a map only hints at the varied contours and ...
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The book presents a hands-on introductory guide to DNA sequence analysis. This can be depicted as a linear map of As, Cs, Gs, and Ts; however, such a map only hints at the varied contours and crevices, twists, kinks, loops, and nodes of the extraordinary double helix. The book uncovers why Perl is the language of choice when identifying patterns in strings of text. It offers a simplified approach to programming that is applicable to biological sequence analysis, especially geared to those who do not have prior programming experience. Concepts include good programming practices, creative approaches to teaching and working with strings and files of sequence data, and sequence related applications of regular expressions, control structures, arrays, and hash tables. A linguistic metaphor is used throughout the text to complement an exceptionally friendly and pedagogically sound introduction to sequence analysis via Perl programming.Less

Perl for Exploring DNA

Mark D. LeBlancBetsey Dexter Dyer

Published in print: 2007-08-23

The book presents a hands-on introductory guide to DNA sequence analysis. This can be depicted as a linear map of As, Cs, Gs, and Ts; however, such a map only hints at the varied contours and crevices, twists, kinks, loops, and nodes of the extraordinary double helix. The book uncovers why Perl is the language of choice when identifying patterns in strings of text. It offers a simplified approach to programming that is applicable to biological sequence analysis, especially geared to those who do not have prior programming experience. Concepts include good programming practices, creative approaches to teaching and working with strings and files of sequence data, and sequence related applications of regular expressions, control structures, arrays, and hash tables. A linguistic metaphor is used throughout the text to complement an exceptionally friendly and pedagogically sound introduction to sequence analysis via Perl programming.

This book presents an integrative approach tomathematical and statistical modelling in ecology and evolutionary biology. After an introductory chapter, the book devotes one chapter for movement ...
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This book presents an integrative approach tomathematical and statistical modelling in ecology and evolutionary biology. After an introductory chapter, the book devotes one chapter for movement ecology, one for population ecology, one for community ecology, and one for genetics and evolutionary ecology. Each chapter starts with a conceptual section, which provides the necessary biological background and motivates the modelling approaches. The next three sections present mathematical modelling approaches, followed by one section devoted to statistical approaches. Each chapter ends with a perspectives section, which summarizes the key messages and discusses the limitations of the approaches considered. To illustrate how the very same modelling approaches apply in different fields of ecology and evolutionary biology, the book uses movement models as a building block to construct single-species models of population dynamics, the models of which are further expanded to models of species communities and to models of evolutionary dynamics. In all chapters, the book starts by making assumptions at the level of individuals, leading to individual-based simulationmodels. To derive analytical insights and to compare the behaviours of different types of models, the book shows how the individual-based models can be simplified, e.g. to yield models formulated directly at the population level. The book has a special emphasis on the integration of models with data. To achieve this, it applies statistical methods to data generated by mathematical models, and thus asks to what extent does the data contain signals of the underlying mechanisms.Less

Otso OvaskainenHenrik Johan de KnegtMaria del Mar Delgado

Published in print: 2016-05-01

This book presents an integrative approach tomathematical and statistical modelling in ecology and evolutionary biology. After an introductory chapter, the book devotes one chapter for movement ecology, one for population ecology, one for community ecology, and one for genetics and evolutionary ecology. Each chapter starts with a conceptual section, which provides the necessary biological background and motivates the modelling approaches. The next three sections present mathematical modelling approaches, followed by one section devoted to statistical approaches. Each chapter ends with a perspectives section, which summarizes the key messages and discusses the limitations of the approaches considered. To illustrate how the very same modelling approaches apply in different fields of ecology and evolutionary biology, the book uses movement models as a building block to construct single-species models of population dynamics, the models of which are further expanded to models of species communities and to models of evolutionary dynamics. In all chapters, the book starts by making assumptions at the level of individuals, leading to individual-based simulationmodels. To derive analytical insights and to compare the behaviours of different types of models, the book shows how the individual-based models can be simplified, e.g. to yield models formulated directly at the population level. The book has a special emphasis on the integration of models with data. To achieve this, it applies statistical methods to data generated by mathematical models, and thus asks to what extent does the data contain signals of the underlying mechanisms.

Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under ...
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Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under changing environments. It stems from an integration of high throughput data from functional genomics, statistical modelling and bioinformatics, and the procedure of phylogeny-based analysis. This book summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution. The book reviews the recent developments in methodology from an evolutionary perspective of genome function, and incorporates substantial examples from high throughput data in model organisms. In addition to phylogeny-based functional analysis of DNA sequences, the book includes discussion on how new types of functional genomic data (e.g., microarray) can provide exciting new insights into the evolution of genome function, which can lead in turn to an understanding of the emergence of genome complexity during evolution.Less

Statistical Theory and Methods for Evolutionary Genomics

Xun Gu

Published in print: 2010-11-04

Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under changing environments. It stems from an integration of high throughput data from functional genomics, statistical modelling and bioinformatics, and the procedure of phylogeny-based analysis. This book summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution. The book reviews the recent developments in methodology from an evolutionary perspective of genome function, and incorporates substantial examples from high throughput data in model organisms. In addition to phylogeny-based functional analysis of DNA sequences, the book includes discussion on how new types of functional genomic data (e.g., microarray) can provide exciting new insights into the evolution of genome function, which can lead in turn to an understanding of the emergence of genome complexity during evolution.

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